Jimmy Oh wins Open Source Research Prize

Jimmy Oh has been awarded the
Catalyst IT/Dragonfly Open Research Prize for his work on
the TableToLongForm package.

Jimmy is a PhD student at
the University of Auckland in the Department of Statistics.
His research examines methods to make open data more
accessible to a wider audience, ultimately culminating in a
tool (or tools) to make the process of obtaining, processing
and using open data easy.

Jimmy presented his work on
the TableToLongForm package at the 2013 “Analytics for a
Changing World: From Data to Decisions” conference jointly
held by the New Zealand Statistical Association and the
Operations Research Society of New Zealand. The conference
was held between 24-27 November 2013, at the University of
Waikato in Hamilton.

The $500 award is sponsored by
Catalyst IT and Dragonfly Science. Catalyst is a specialist
in free and open source technologies. Catalyst's mission is
to make open source the preferred technology choice of New
Zealand (see www.catalyst.net.nz). Dragonfly Science
carries out independent statistical modelling and analysis
(see www.dragonfly.co.nz).

To be eligible for the Open
Source Research Prize, each stage of the research process
had to be carried out with open source tools. After careful
consideration of all the talks at this conference, the
judges decided to award the Open Source Research Prize to
Jimmy Oh. Jimmy's TableToLongForm package made it easier
to access open data sources, which furthered the goals of
open source research and open data.

Catalyst were
delighted to hear that Open Source Research Prize encouraged
Jimmy to continue developing TableToLongForm, whose initial
plans were to shelve it for a few months to focus on other
projects.

TableToLongForm is an R package that
automatically converts hierarchical Tables intended for a
human reader into a simple LongForm Dataframe that is
machine readable, hence enabling much greater utilisation of
the data. It does this by recognising positional cues
present in the hierarchical Table (which would normally be
interpreted visually by the human brain) to decompose, then
reconstruct the data into a LongForm Dataframe.

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